Bayesian analysis of transformation latent variable models with multivariate censored data

Transformation latent variable models are proposed in this study to analyze multivariate censored data. The proposed models generalize conventional linear transformation models to semiparametric transformation models that accommodate latent variables. The characteristics of the latent variables were...

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Bibliographic Details
Published inStatistical methods in medical research Vol. 25; no. 5; p. 2337
Main Authors Song, Xin-Yuan, Pan, Deng, Liu, Peng-Fei, Cai, Jing-Heng
Format Journal Article
LanguageEnglish
Published England 01.10.2016
Subjects
Algorithms
Bayes Theorem
Cardiovascular Diseases
Diabetes Mellitus, Type 2
Female
Humans
Male
Markov Chains
Models, Statistical
Monte Carlo Method
Multivariate Analysis
semiparametric model
transformation model
latent variables
Markov chain Monte Carlo methods
Bayesian P-splines
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Summary:Transformation latent variable models are proposed in this study to analyze multivariate censored data. The proposed models generalize conventional linear transformation models to semiparametric transformation models that accommodate latent variables. The characteristics of the latent variables were assessed based on several correlated observed indicators through measurement equations. A Bayesian approach was developed with Bayesian P-splines technique and the Markov chain Monte Carlo algorithm to estimate the unknown parameters and transformation functions. Simulation shows that the performance of the proposed methodology is satisfactory. The proposed method was applied to analyze a cardiovascular disease data set.
ISSN:1477-0334
DOI:10.1177/0962280214522786